Fine-tuned small LLMs can beat large ones with programmatic data curation

Fine-tuned small language models can achieve performance comparable to larger models at dramatically lower costs through programmatic data curation. Research demonstrates that models like Gemini 2.0 Flash Lite can deliver up to 30x cost reduction while maintaining competitive performance across tasks including data extraction, navigation, and customer service applications. This approach also yields 2-4x faster response times, making it ideal for production environments where inference costs quickly accumulate.